Mikado commands

Mikado documentation

1 Run mikado configure to generate the configuration and scoring files

cd /home/train/Annotation_workshop/Mikado/Chr3-1065466-1464870/6_Full_Mikado

(
mikado configure \
    --full \
    --list list.txt \
    --reference Inputs/Assembly/Athaliana_447_TAIR10.Chr3.fa \
    --junctions Inputs/Junctions/portcullis.pass.junctions.regionA.bed \
    -bt Inputs/Homology/selected_species.fa \
    --scoring plant.yaml \
    --copy-scoring plant.yaml \
    configuration.toml
)

⚠️ Windows users:

mikado configure --full --list list.txt --reference Inputs/Assembly/Athaliana_447_TAIR10.Chr3.fa --junctions Inputs/Junctions/portcullis.pass.junctions.regionA.bed -bt Inputs/Homology/selected_species.fa --scoring plant.yaml --copy-scoring plant.yaml configuration.toml

2 Run mikado prepare to remove redundancy, fix or remove problematic transcripts and extract the transcript sequences

mikado prepare -p 1 --json-conf configuration.toml

3 Run Prodigal to call ORFs

prodigal -g 1 -f gff -i mikado_prepared.fasta -o mikado_prepared.cds.gff

4 Run diamond for homology scores

(
diamond blastx \
    --threads 1 \
    -q mikado_prepared.fasta \
    -d Homology/selected_species \
    -o mikado_prepared_matches.tsv \
    --outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore ppos btop
)

⚠️ Windows users:

diamond blastx --threads 1 -q mikado_prepared.fasta -d Homology/selected_species -o mikado_prepared_matches.tsv --outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore ppos btop

5 Run mikado serialise to load ORFs, homology hits and junctions to the database

(
mikado serialise \
    -p 1 \
    --tsv mikado_prepared_matches.tsv \
    --orfs mikado_prepared.cds.gff \
    --json-conf configuration.toml
)

⚠️ Windows users:

mikado serialise -p 1 --tsv mikado_prepared_matches.tsv --orfs mikado_prepared.cds.gff --json-conf configuration.toml

6 Run mikado pick to identify gene loci and select the best transcripts

(
mikado pick \
    -p 1 \
    --subloci-out mikado.subloci.gff3 \
    --monoloci-out mikado.monoloci.gff3 \
    --output-dir MyOutput \
    --json-conf configuration.toml
)

⚠️ Windows users:

mikado pick -p 1 --subloci-out mikado.subloci.gff3 --monoloci-out mikado.monoloci.gff3 --output-dir MyOutput --json-conf configuration.toml

7 Run mikado compare to compare our selected mikado pick transcripts to the reference genemodels

mikado compare -r Athaliana_447_Araport11.gene_exons.regionA.gtf -p MyOutput/mikado.loci.gff3

8 Run mikado util stats to generate the gene stats for the mikado genemodels and the reference genemodels

mikado util stats MyOutput/mikado.loci.gff3 mikado.loci.gff3.stats

mikado util stats Athaliana_447_Araport11.gene_exons.regionA.gtf Athaliana_447_Araport11.gene_exons.regionA.gtf.stats

9 Side by side comparison of the stats

(
paste <(cut -f1,2,3 Athaliana_447_Araport11.gene_exons.regionA.gtf.stats |sed  "1i Reference\t") \
    <(cut -f2,3 mikado.loci.gff3.stats |sed  "1i Mikado") \
    | tabulate -1 -s '\t' -f grid \
    | tee Reference_and_Mikado.stats
)

⚠️ Windows users:

paste <(cut -f1,2,3 Athaliana_447_Araport11.gene_exons.regionA.gtf.stats |sed "1i Reference\t") <(cut -f2,3 mikado.loci.gff3.stats |sed "1i Mikado") | tabulate -1 -s '\t' -f grid | tee Reference_and_Mikado.stats

10 Try editing the scoring or configuration file OR using one of the alternative configuration and scoring files

(
mikado pick \
    -p 1 \
    --subloci-out mikado.subloci.gff3 \
    --monoloci-out mikado.monoloci.gff3 \
    --output-dir MyOutput2 \
    --json-conf Alt_configs/reat_transcriptome_plants_configuration.toml \
    --scoring-file Alt_configs/reat_transcriptome_plants_scoring.yaml
)

⚠️ Windows users:

mikado pick -p 1 --subloci-out mikado.subloci.gff3 --monoloci-out mikado.monoloci.gff3 --output-dir MyOutput2 --json-conf Alt_configs/reat_transcriptome_plants_configuration.toml --scoring-file Alt_configs/reat_transcriptome_plants_scoring.yaml

Does this improve the gene models?

To clean up the directory back to the original starting files:

rm -rf MyOutput* \
    configuration.toml plant.yaml \
    mikado.db* \
    mikado_prepared* \
    mikado.subloci.gff3 mikado.monoloci.gff3 \
    mikado_compare* \
    *.midx \
    *.stats \
    Reference_and_Mikado.stats \
    prepare.log \
    serialise.log

⚠️ Windows users:

rm -rf MyOutput* configuration.toml plant.yaml mikado.db* mikado_prepared* mikado.subloci.gff3 mikado.monoloci.gff3 mikado_compare* *.midx *.stats Reference_and_Mikado.stats prepare.log serialise.log

Notes

The entire process can be run using the provided bash script:

./commands.sh

• You will be prompted to provide alternative configuration and scoring files.
  • Press Enter to use the indicated default values.
• For further exploration, try rerunning the pipeline using the alternative configuration and scoring files located in Alt_configs/ e.g.

 Alt_configs/reat_transcriptome_plants_scoring.yaml

 Alt_configs/reat_transcriptome_plants_configuration.toml

Note provide the path to the file not just the file name

• Compare the resulting outputs using the mikado compare stats to observe how different configurations affect the results.